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检索条件"机构=Computer Science Applied Computer Technology"

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An Adaptive Streaming Feature Selection Technique for Classifying Non-Stationary Data Streams 7

An Adaptive Streaming Feature Selection Technique for Classi...

引用

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Arya, Monika Dewangan, Bhupesh Kumar Choudhury, Tanupriya Kotecha, Ketan Dewangan, Sanjana Gaidhane, Akhilesh Bhilai Institute of Technology Department of Computer Science and Engineering Durg India Op Jindal University Department of Computer Science and Engineering Raigarh496109 India Pune411045 India Symbiosis Centre for Applied Artificial Intelligence Pune411045 India School of Science Op Jindal University Raigarh India Sages M.G. Ward Department of Mathematics Kondagaon India

ISBN: (纸本)9798350342154

In today's digital era, various real-world applications generate data in streams, and these data streams are of two types stationary data streams, which are static, and non-stationary data streams that are dynamic in nature. Non-stationary data streams are pro-to-concept drifts. To overcome this issue, an adaptable streaming feature selection technique for non-stationary data streams is proposed in this paper. The selection of relevant feature space from non-stationary data streams is crucial for enabling intelligence through various data mining tasks, specifically using data classification. Feature selection improves the overall performance of the classifier while increasing the computational overhead. The proposed approach aims to enable streaming feature selection whenever the classification accuracy is underperforming to enhance the classification accuracy, while the model remains stable if there is no change in the concepts. The proposed approach is adaptive and ameliorates the classifier performance by reducing the error rate during changes in the concept with time. This adaptability to concept drift improves the accuracy by approximately 16% and reduces the computation time by 50%. The proposed model avoids overfitting of the model. © 2023 IEEE.

关键词： Feature Selection

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Extracting Key Information from Shopping Receipts by Using Bayesian Deep Learning via Multi-modal Features 3rd

Extracting Key Information from Shopping Receipts by Using...

引用

3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Chen, Jiaqi Shao, Lujiao Zhou, Haibin Ma, Jianghong Meng, Weizhi Wang, Zenghui Zhang, Haijun Departmant of Computer Science Harbin Institudte of Technology Shenzhen China Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Electrical and Mining Engineering University of South Africa Florida1710 South Africa

ISBN: (纸本)9789811961410

This research presents a new key information extraction algorithm from shopping receipts. Specifically, we train semantic, visual and structural features through three deep learning methods, respectively, and formulate rule features according to the characteristics of shopping receipts. Then we propose a multi-class text classification algorithm based on multi-modal features using Bayesian deep learning. After post-processing the output of the classification algorithm, the key information we seek for can be obtained. Our algorithm was trained on a self-labeled Chinese shopping receipt dataset and compared with several baseline methods. Extensive experimental results demonstrate that the proposed method achieves optimal results on our Chinese receipt dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Chou, Ka Seng Wong, Kei Long Aguiari, Davide Tse, Rita Tang, Su-Kit Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna Italy Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates UCLA Samueli Computer Science University of California Los Angeles Los Angeles United States

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more than 2.3 million discharging samples via a custom Internet of Thing device for more than one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Lithium-ion batteries

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Toddler Activity Analysis Using Machine Learning

Toddler Activity Analysis Using Machine Learning

引用

Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： I. Kala Bhuvaneswari P Dharun R Dharshini S Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

Toddler activity recognition aims in finding out the activities of toddler and also toddlers based on the video captured by camera. The purpose of activity recognition is to monitor a new employee of the company (maybe a software or even a restaurant) whether employees are working properly or do we need to give more training. This system can also be used by the parents to monitor the activity of the kids who are alone at home. This system can also be implemented in hospitals to monitor the patients and to check their improvement. Toddler activity recognition system is developed using CNN and LSTM. To predict the activity the path of the video file is given as input. The predicted result by the model is checked for accuracy. To make the result available for the end user (who needs to monitor the activity) and can access the result through two ways. In the First method user can directly view on the system where the model is predicting the result. In the second method user is out of station, so the predicted result will be updated on the cloud on fixed time intervals and user can view the result from the working place itself.

关键词： Training Pediatrics Surveillance Machine learning Activity recognition Predictive models Cameras

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Smart Spaces: Occupancy Detection using Adaptive Back-Propagation Neural Network 2

Smart Spaces: Occupancy Detection using Adaptive Back-Propag...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Saleem, Muhammad Khan, Muhammad Saeed Issa, Ghassan F. Khadim, Assma Asif, Muhammad Akram, Ali Sheraz Nair, Harish K G School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Skyline University College School of Information Technology Sharjah United Arab Emirates Skyline University College School of IT University City Sharjah Sharjah1797 United Arab Emirates

ISBN: (纸本)9798350335644

In recent years, the concept of smart spaces and occupancy detection is crucial for creating more efficient and sustainable buildings that prioritize both energy efficiency and occupant comfort. By leveraging advanced technologies and real-time data analysis, building managers can optimize energy consumption, reduce operating costs, and improve the overall user experience of the space. One important aspect of this optimization process is the detection of occupancy in smart buildings, which can help to control various systems like Heating, Ventilation, Air Conditioning (HVAC) and lighting. Accurate occupancy detection is a key factor in reducing energy consumption and cost optimization for building control systems, particularly in the areas of HVAC and lighting. To this end, Adaptive Back-Propagation Neural Network (ABPNN) algorithms is applied to a dataset generated by CO2, humidity, temperature, and light sensors to enhance the accuracy of occupancy detection in smart *** this study, the ABPNN based Bayesian Regularization (BR) algorithm is found to have the highest precision rate, with 70% (5701 samples) of the dataset used for training, 15% (1221 samples) for validation, and 15% (1221 samples) for testing. Furthermore, the BR algorithm demonstrated an impressive accuracy rate of 99.8% when compared to other algorithms. Smart spaces with advanced sensors and BR algorithms, such as the BR algorithm, accurately detects occupancy and optimize systems like HVAC and lighting to improve energy efficiency and occupant comfort. This reduces energy consumption and operating costs, while providing insights for optimizing space usage and improving overall building performance. © 2023 IEEE.

关键词： Carbon dioxide

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Fake Product Identification using Blockchain

Fake Product Identification using Blockchain

引用

International Conference on Computing and Networking technology (ICCNT)

作者： S Hemkiran RM Prakash Ramanathan Mohammad Mudasir War KS Aadhithiyan Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

Manufacture and sale of counterfeit goods has been plaguing several business sectors for many years. The production of counterfeit goods are increasing, this has an impact on the sales and earnings of the businesses. By stifling revenues, economic growth, and consumer health, it has an impact on governments, global trade, businesses, and consumers. The current technology used for combating counterfeit goods rely on a centralised authority. Issues with this design include single point processing, failure, and storage. Blockchain technology has can be used as an alternative to address all these issues for these *** code can be used along with a blockchain hash code to uniquely identify every single product that ever enters the supply chain, hence no fake product can flood the supply chain. Ethereum blockchain is utilized and smart contracts are deployed to store the data about each and every product. Each products whole history can be tracked by using its QR code.

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Synergistic Neural Matrix Factorization: Elevating Complementary Product Recommendations in E-Commerce using Deep Neural Networks

Synergistic Neural Matrix Factorization: Elevating Complemen...

引用

Delhi Section Conference (DELCON), IEEE

作者： Neha Gupta Vaishali Joshi Aishwarya Chourey Ekta Acharya School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh India Computer Section Jai Narain Vyas University Jodhpur Rajasthan India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This research explores the submission of deep neural networks (DNNs) to enhance corresponding product references in e-commerce stages. By leveraging the Neural Collaborative Filtering (NeuMF) model, which integrates Comprehensive Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP), the study captures both linear and non-linear user-item connections to improve recommendation accuracy. Key performance metrics, including accuracy, loss, ROC curve, and precision-recall curve, were analyzed to assess the model's effectiveness. Results show that the NeuMF model significantly improves recommendation accuracy and user satisfaction, with an AUC of 0.94. However, the precision-recall analysis highlights areas for improvement, mainly in handling imbalanced datasets. This work demonstrates the potential of advanced neural models to drive better user engagement and increase sales in dynamic e-commerce environments while suggesting future directions for optimizing precision-recall trade-offs.

关键词： Measurement Analytical models Accuracy Refining Data preprocessing Artificial neural networks Machine learning Data models Electronic commerce Context modeling

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Home Automation with Webserver Deployment with Raspberry Pi Pico

Home Automation with Webserver Deployment with Raspberry Pi ...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： C P Shabariram M Venkateshwaran R Abinesh R Manoranjith Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

Home Automation system with web server deployment is developed using Raspberry Pi Pico and micropython, which turns ON the lights based on the count of persons inside that particular room. The system uses Infrared Red (IR) sensor, which detects the heat radiation in the environment. This heat radiation is not constant as it changes due to the movement of people in the sensing environment. This change is used as a measure to detect the changes in the environment which allows us to perform a particular action for the event. The data is sent to node which sends the data to the webserver running in the Raspberry pi pico. Based on the result, the light is turned ON or OFF.

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Defective Microstrip Structure Based Phase Shifter with Reduced Size for Phased Antenna Array Applications 12

Defective Microstrip Structure Based Phase Shifter with Redu...

引用

12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Khan, Hajra Khan, Shahid Nasir, Jamal Ali Shah, Syed Ahson Dalarsson, Mariana Alibakhshikenari, Mohammad Comsats University Islamabad Department of Electrical and Computer Engineering Pakistan Hanyang University Applied Bioelectronics Laboratory Seoul Korea Republic of School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Spain

ISBN: (纸本)9798350358056

In this work, we propose a small-size and low-cost phase shifter based on defective microstrip structure (DMS) technique, with a modified reconfigurable unit cell (MRDMS) for WLAN applications at 5.2 GHz. The phase shifter is made up of an MRDMS unit, created by etching slots on a simple microstrip line with an outline of three bridges. To further increase the phase shift, a T-shaped slot is introduced in the central bridge and PIN diodes on the edges of the outer bridges are used. By switching the diodes on and off, the MRDMS unit cell is capable of operating in a variety of different modes to give a phase shift of 37.71° at 5.2 GHz. Additionally, MRDMS unit cells are cascaded for higher phase shift as required. Cascaded two- and four-cell phase shifters were designed and simulated in HFSS and a parametric study on various parameters was conducted to show the detailed operating properties of the proposed phase shifter. Compared to previously reported research, the present study shows that two cascaded phase shifters introduce 75.08° phase shifts and 151.75°, respectively. To the best of our knowledge, this is the maximum phase shift obtained by a phase shifter based on MRDMS. © 2023 IEEE.

关键词： defected microstrip structure phase shifter reconfigurable microwave system

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Simulating Home Appliance Behavior and Operational Condition-Action Rules for Autonomous Intelligence

Simulating Home Appliance Behavior and Operational Condition...

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Intelligent Computing and Next Generation Networks（ICNGN), International Conference on

作者： Kennedy Efosa Ehimwenma Haowen Ji Lin Jinle Safiya Al Sharji Maryam Cheraghy Yu Bowen Dept of Computer Science College of Science Mathematics & Technology Wenzhou Kean University Wenzhou China Information Technology Dept University of Technology & Applied Sciences Muscat Oman

ISBN: (数字)9798331529222

ISBN: (纸本)9798331529239

The future of home appliances in the IoT (Internet of Things) lies in self-awareness, independent condition-action rule implementation, and device autonomy. In the concept of autonomy, agent-based IoT appliances can SLEEP and WAKE-UP independently as when necessary, perceive and compute data; and engage in communication or negotiation with other agent-based devices. In the paradigm of OOP, this paper describes various home appliances' autonomous artificial intelligent (AAI) behavior. While self-awareness borders on the appliance knowing its current status and location, self-control relates to the appliance's ability to control its outgoing and incoming data and take the appropriate action based on some given environment data conditional rules. Thus, this paper presents some IoT home appliances' operational condition-action behavior (CAB). Firstly, by simulation, then followed by the actual transformation of the CAB rules into modular programs in object-oriented paradigm for sensor and appliance agents. The WindSpeedSensor, TempSensor, and HumiditySensor utilize the given environment data input to communicate with their respective WindowAppliance and CoffeeMachineAppliance agent, as demonstrated in this paper. In turn, the appliance agents take the decision reached by their sensors to perform some autonomous action. This model depicts independent AAI behavior of home appliances for the benefit of home owners.

关键词： Home appliances Object oriented modeling Computational modeling Internet of Things Next generation networking

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